Independently Trained Multi-Scale Registration Network Based on Image Pyramid.
Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anato...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1557 - 1567 |
|---|---|
| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179554120&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554120 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554120 179554120 179554120 10.1007/s10278-024-01019-8 179554120 ppf: 1557 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Independently Trained Multi-Scale Registration Network Based on Image Pyramid. aug: au: Chang, Qing Wang, Yaqi Zhang, Jieming affil: https://ror.org/01vyrm377 School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China sug: subj: Heart Radiography Tomography, X-Ray Computed Image Interpretation, Computer Assisted Neural Networks (Computer) Deep Learning Algorithms Evaluation Human Funding Source Registration Image Processing, Computer Assisted ab: Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of different resolutions as the training set. Using image pairs of different resolutions, train each registration network independently to extract image features from the image pairs at different resolutions. During the testing stage, the large deformation registration is decomposed into a multi-scale registration process. The deformation fields of different resolutions are fused by a step-by-step deformation method, thereby addressing the challenge of directly handling large deformations. Experiments were conducted on the open cardiac dataset ACDC (Automated Cardiac Diagnosis Challenge); the proposed method achieved an average Dice score of 0.828 in the experimental results. Through comparative experiments, it has been demonstrated that the proposed method effectively addressed the challenge of heart image registration and achieved superior registration results for cardiac images. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|